scLANE Simulation Study - Trajectory DE Method Comparisonlibrary(dplyr)
library(ggplot2)
library(targets)source("R/functions_analysis.R")tar_load(metric_table_master)filter(metric_table_master,
MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>%
ggplot(aes(x = ROC_AUC, y = SIM_REFERENCE, color = MODEL_TYPE, fill = MODEL_TYPE)) +
facet_wrap(~MODEL_TYPE) +
ggridges::geom_density_ridges(alpha = 0.6, scale = 0.95, linewidth = 1) +
scale_x_continuous(labels = scales::label_percent()) +
labs(y = "Reference Dataset", x = "ROC-AUC") +
theme_analysis() +
theme(legend.position = "none")filter(metric_table_master,
MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>%
ggplot(aes(x = F_MEASURE, y = SIM_REFERENCE, color = MODEL_TYPE, fill = MODEL_TYPE)) +
facet_wrap(~MODEL_TYPE) +
ggridges::geom_density_ridges(alpha = 0.6, scale = 0.95, linewidth = 1) +
scale_x_continuous(labels = scales::label_number(accuracy = 0.1),
limits = c(NA, 1)) +
labs(y = "Reference Dataset", x = "F-measure") +
theme_analysis() +
theme(legend.position = "none")filter(metric_table_master,
MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>%
mutate(N_CELLS = round(N_CELLS, digits = -1),
N_CELLS = as.factor(N_CELLS),
RUNTIME_MINS = RUNTIME_HOURS * 60) %>%
ggplot(aes(x = N_CELLS, y = RUNTIME_MINS, color = MODEL_TYPE)) +
geom_boxplot() +
scale_y_continuous(labels = scales::label_number(accuracy = 1, suffix = "min")) +
labs(x = "Cells", y = "Runtime") +
theme_analysis() +
theme(legend.title = element_blank())filter(metric_table_master,
MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>%
mutate(RUNTIME_MINS = RUNTIME_HOURS * 60,
GENES_PER_MIN = N_GENES / RUNTIME_MINS) %>%
ggplot(aes(x = N_CELLS, y = GENES_PER_MIN, color = MODEL_TYPE, fill = MODEL_TYPE)) +
geom_smooth(method = "lm", alpha = 0.25) +
scale_y_continuous(labels = scales::label_number(accuracy = 1)) +
labs(x = "Cells", y = "Genes per Minute") +
theme_analysis() +
theme(legend.title = element_blank()) +
guides(color = guide_legend(override.aes = list(alpha = 0, linewidth = 2)))filter(metric_table_master,
MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>%
mutate(RUNTIME_MINS = RUNTIME_HOURS * 60,
GENES_PER_MIN = N_GENES / RUNTIME_MINS) %>%
ggplot(aes(x = GENES_PER_MIN, color = MODEL_TYPE, fill = MODEL_TYPE)) +
geom_density(alpha = 0.3, linewidth = 1) +
scale_x_continuous(labels = scales::label_number(accuracy = 1)) +
labs(x = "Genes per Minute", y = "Density") +
theme_analysis() +
theme(legend.title = element_blank()) +
guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))filter(metric_table_master,
MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>%
mutate(N_CELLS = round(N_CELLS, digits = -1),
N_CELLS = as.factor(N_CELLS),
MEM_USED_GB = MEM_USED / 1000) %>%
ggplot(aes(x = N_CELLS, y = MEM_USED_GB, color = MODEL_TYPE)) +
geom_boxplot() +
scale_y_continuous(labels = scales::label_number(suffix = "gb")) +
labs(x = "Cells", y = "Memory Usage") +
theme_analysis() +
theme(legend.title = element_blank())filter(metric_table_master,
MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>%
mutate(MEM_USED_GB = MEM_USED / 1000) %>%
ggplot(aes(x = MEM_USED_GB, color = MODEL_TYPE, fill = MODEL_TYPE)) +
geom_density(alpha = 0.3, linewidth = 1) +
scale_x_continuous(labels = scales::label_number(suffix = "gb")) +
labs(x = "Memory Usage", y = "Density") +
theme_analysis() +
theme(legend.title = element_blank()) +
guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))filter(metric_table_master,
MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>%
mutate(N_CELLS = round(N_CELLS, digits = -1),
N_CELLS = as.factor(N_CELLS)) %>%
ggplot(aes(x = N_CELLS, y = F_MEASURE, color = MODEL_TYPE)) +
geom_boxplot() +
scale_y_continuous(labels = scales::label_number(accuracy = 0.1)) +
labs(x = "Cells", y = "F-measure") +
theme_analysis() +
theme(legend.title = element_blank())filter(metric_table_master,
MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>%
ggplot(aes(x = F_MEASURE, color = MODEL_TYPE, fill = MODEL_TYPE)) +
geom_density(alpha = 0.3, linewidth = 1) +
scale_x_continuous(labels = scales::label_number(accuracy = 0.1)) +
labs(x = "F-measure", y = "Density") +
theme_analysis() +
theme(legend.title = element_blank()) +
guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))filter(metric_table_master,
MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>%
mutate(N_CELLS = round(N_CELLS, digits = -1),
N_CELLS = as.factor(N_CELLS)) %>%
ggplot(aes(x = N_CELLS, y = BAL_ACCURACY, color = MODEL_TYPE)) +
geom_boxplot() +
scale_y_continuous(labels = scales::label_percent(accuracy = 1)) +
labs(x = "Cells", y = "Balanced Accuracy") +
theme_analysis() +
theme(legend.title = element_blank())filter(metric_table_master,
MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>%
ggplot(aes(x = BAL_ACCURACY, color = MODEL_TYPE, fill = MODEL_TYPE)) +
geom_density(alpha = 0.3, linewidth = 1) +
scale_x_continuous(labels = scales::label_percent(accuracy = 1)) +
labs(x = "Balanced Accuracy", y = "Density") +
theme_analysis() +
theme(legend.title = element_blank()) +
guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))filter(metric_table_master,
MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>%
mutate(N_CELLS = round(N_CELLS, digits = -1),
N_CELLS = as.factor(N_CELLS)) %>%
ggplot(aes(x = N_CELLS, y = RECALL, color = MODEL_TYPE)) +
geom_boxplot() +
scale_y_continuous(labels = scales::label_percent(accuracy = 1)) +
labs(x = "Cells", y = "Recall") +
theme_analysis() +
theme(legend.title = element_blank())filter(metric_table_master,
MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>%
ggplot(aes(x = RECALL, color = MODEL_TYPE, fill = MODEL_TYPE)) +
geom_density(alpha = 0.3, linewidth = 1) +
scale_x_continuous(labels = scales::label_percent(accuracy = 1)) +
labs(x = "Recall", y = "Density") +
theme_analysis() +
theme(legend.title = element_blank()) +
guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))filter(metric_table_master,
MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>%
mutate(N_CELLS = round(N_CELLS, digits = -1),
N_CELLS = as.factor(N_CELLS)) %>%
ggplot(aes(x = N_CELLS, y = ACCURACY)) +
geom_boxplot(aes(color = MODEL_TYPE)) +
scale_y_continuous(labels = scales::label_percent(accuracy = 1)) +
labs(x = "Cells", y = "Accuracy") +
theme_analysis() +
theme(legend.title = element_blank())filter(metric_table_master,
MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>%
ggplot(aes(x = ACCURACY, color = MODEL_TYPE, fill = MODEL_TYPE)) +
geom_density(alpha = 0.3, linewidth = 1) +
scale_x_continuous(labels = scales::label_percent(accuracy = 1)) +
labs(x = "Accuracy", y = "Density") +
theme_analysis() +
theme(legend.title = element_blank()) +
guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))filter(metric_table_master,
MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>%
mutate(N_CELLS = round(N_CELLS, digits = -1),
N_CELLS = as.factor(N_CELLS)) %>%
ggplot(aes(x = N_CELLS, y = ROC_AUC, color = MODEL_TYPE)) +
geom_boxplot() +
scale_y_continuous(labels = scales::label_percent(accuracy = 1)) +
labs(x = "Cells", y = "ROC-AUC") +
theme_analysis() +
theme(legend.title = element_blank())filter(metric_table_master,
MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>%
ggplot(aes(x = ROC_AUC, color = MODEL_TYPE, fill = MODEL_TYPE)) +
geom_density(alpha = 0.3, linewidth = 1) +
scale_x_continuous(labels = scales::label_percent(accuracy = 1)) +
labs(x = "ROC-AUC", y = "Density") +
theme_analysis() +
theme(legend.title = element_blank()) +
guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))filter(metric_table_master,
MODEL_TYPE %in% c("scLANE - GLM")) %>%
pull(ROC_CURVE) %>%
purrr::reduce(rbind) %>%
left_join((distinct(metric_table_master, DATASET_NAME, N_CELLS)),
by = c("dataset" = "DATASET_NAME")) %>%
mutate(N_CELLS = round(N_CELLS, digits = -1),
N_CELLS = as.factor(N_CELLS)) %>%
ggplot(aes(x = 1 - specificity, y = sensitivity, group = dataset, color = N_CELLS)) +
facet_wrap(~paste0("Cells: ", N_CELLS)) +
geom_segment(x = 0, xend = 0, y = 1, yend = 1, color = "black", linetype = "dashed", size = 1) +
geom_line(size = 1, alpha = 0.8) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
labs(x = "1 - Specificity",
y = "Sensitivity",
color = "Cells",
title = "scLANE - GLM") +
theme_analysis() +
guides(color = guide_legend(override.aes = list(linewidth = 2, alpha = 1)))filter(metric_table_master,
MODEL_TYPE %in% c("tradeSeq")) %>%
pull(ROC_CURVE) %>%
purrr::reduce(rbind) %>%
left_join((distinct(metric_table_master, DATASET_NAME, N_CELLS)),
by = c("dataset" = "DATASET_NAME")) %>%
mutate(N_CELLS = round(N_CELLS, digits = -1),
N_CELLS = as.factor(N_CELLS)) %>%
ggplot(aes(x = 1 - specificity, y = sensitivity, group = dataset, color = N_CELLS)) +
facet_wrap(~paste0("Cells: ", N_CELLS)) +
geom_segment(x = 0, xend = 0, y = 1, yend = 1, color = "black", linetype = "dashed", size = 1) +
geom_line(size = 1, alpha = 0.8) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
labs(x = "1 - Specificity",
y = "Sensitivity",
color = "Cells",
title = "tradeSeq") +
theme_analysis() +
guides(color = guide_legend(override.aes = list(linewidth = 2, alpha = 1)))filter(metric_table_master,
MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>%
mutate(N_CELLS = round(N_CELLS, digits = -1),
N_CELLS = as.factor(N_CELLS)) %>%
ggplot(aes(x = N_CELLS, y = AUC_PR, color = MODEL_TYPE)) +
geom_boxplot() +
scale_y_continuous(labels = scales::label_percent(accuracy = 1)) +
labs(x = "Cells", y = "PR-AUC") +
theme_analysis() +
theme(legend.title = element_blank())filter(metric_table_master,
MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>%
ggplot(aes(x = AUC_PR, color = MODEL_TYPE, fill = MODEL_TYPE)) +
geom_density(alpha = 0.3, linewidth = 1) +
scale_x_continuous(labels = scales::label_percent(accuracy = 1)) +
labs(x = "PR-AUC", y = "Density") +
theme_analysis() +
theme(legend.title = element_blank()) +
guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))filter(metric_table_master,
MODEL_TYPE %in% c("scLANE - GLM")) %>%
pull(PR_CURVE) %>%
purrr::reduce(rbind) %>%
left_join((distinct(metric_table_master, DATASET_NAME, N_CELLS)),
by = c("dataset" = "DATASET_NAME")) %>%
mutate(N_CELLS = round(N_CELLS, digits = -1),
N_CELLS = as.factor(N_CELLS)) %>%
ggplot(aes(x = recall, y = precision, group = dataset, color = N_CELLS)) +
facet_wrap(~paste0("Cells: ", N_CELLS)) +
geom_segment(x = 0, xend = 1, y = 1, yend = 0, color = "black", linetype = "dashed", size = 1) +
geom_line(size = 1, alpha = 0.8) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1)) +
scale_y_continuous(limits = c(0, 1), labels = scales::percent_format(accuracy = 1)) +
labs(x = "Recall",
y = "Precision",
color = "Cells",
title = "scLANE - GLM") +
theme_analysis() +
guides(color = guide_legend(override.aes = list(linewidth = 2, alpha = 1)))filter(metric_table_master,
MODEL_TYPE %in% c("tradeSeq")) %>%
pull(PR_CURVE) %>%
purrr::reduce(rbind) %>%
left_join((distinct(metric_table_master, DATASET_NAME, N_CELLS)),
by = c("dataset" = "DATASET_NAME")) %>%
mutate(N_CELLS = round(N_CELLS, digits = -1),
N_CELLS = as.factor(N_CELLS)) %>%
ggplot(aes(x = recall, y = precision, group = dataset, color = N_CELLS)) +
facet_wrap(~paste0("Cells: ", N_CELLS)) +
geom_segment(x = 0, xend = 1, y = 1, yend = 0, color = "black", linetype = "dashed", size = 1) +
geom_line(size = 1, alpha = 0.8) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1)) +
scale_y_continuous(limits = c(0, 1), labels = scales::percent_format(accuracy = 1)) +
labs(x = "Recall",
y = "Precision",
color = "Cells",
title = "tradeSeq") +
theme_analysis() +
guides(color = guide_legend(override.aes = list(linewidth = 2, alpha = 1)))sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.3.3 (2024-02-29)
os Ubuntu 22.04.5 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/New_York
date 2024-10-07
pandoc 2.9.2.1 @ /usr/bin/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
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AnnotationDbi 1.62.2 2023-07-02 [2] Bioconductor
backports 1.5.0 2024-05-23 [1] CRAN (R 4.2.3)
base64url 1.4 2018-05-14 [1] CRAN (R 4.3.3)
bigassertr 0.1.6 2023-01-10 [1] CRAN (R 4.3.2)
bigparallelr 0.3.2 2021-10-02 [1] CRAN (R 4.3.2)
bigstatsr 1.5.12 2022-10-14 [1] CRAN (R 4.3.2)
Biobase * 2.60.0 2023-04-25 [2] Bioconductor
BiocGenerics * 0.46.0 2023-04-25 [2] Bioconductor
BiocParallel 1.34.2 2023-05-22 [2] Bioconductor
Biostrings 2.68.1 2023-05-16 [2] Bioconductor
bit 4.5.0 2024-09-20 [2] CRAN (R 4.3.3)
bit64 4.5.2 2024-09-22 [2] CRAN (R 4.3.3)
bitops 1.0-8 2024-07-29 [2] CRAN (R 4.3.3)
blob 1.2.4 2023-03-17 [2] CRAN (R 4.3.1)
boot 1.3-31 2024-08-28 [4] CRAN (R 4.3.3)
broom * 1.0.6 2024-05-17 [1] CRAN (R 4.2.3)
broom.mixed 0.2.9.5 2024-04-01 [1] CRAN (R 4.2.3)
bslib 0.8.0 2024-07-29 [2] CRAN (R 4.3.3)
cachem 1.1.0 2024-05-16 [2] CRAN (R 4.3.3)
callr 3.7.6 2024-03-25 [1] CRAN (R 4.2.3)
caTools 1.18.3 2024-09-04 [2] CRAN (R 4.3.3)
circlize 0.4.16 2024-02-20 [1] CRAN (R 4.3.2)
class 7.3-22 2023-05-03 [4] CRAN (R 4.3.1)
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